体内
受体
药理学
药代动力学
肽
化学
阿片肽
血脑屏障
强啡肽
生物
类阿片
内分泌学
生物化学
中枢神经系统
生物技术
出处
期刊:Yakubutsu dōtai
[Japanese Society for the Study of Xenobiotics]
日期:1999-01-01
卷期号:14 (2): 148-157
摘要
In order to provide mechanistic insights into the pharmacokinetics of peptide drugs (including cytokines and growth factors), I investigated the mechanisms underlying the clearance and distribution of opioid peptides (β-endorphin, dynorphin, and dynorphin-like analgesic peptide), human insulin, and synthetic cyclopeptides (cyclosporine and PSC 833), by use of in vivo animals, perfused organs, and in vitro experimental systems. For opioid peptides, their tissue distribution was suggested to be governed by specific binding with K-type opioid receptors present in peripheral tissues including lung and liver, whereas for insulin the distribution and clearance were suggested to be governed by receptor binding and receptor-mediated endocytosis (RME), respectively, at the physiological concentration range in target organs. The “receptorrecycling” model, in which the internalized receptors are recycled back to the surface to be reutilized for subsequent binding, was developed to predict the hepatic handling of insulin in mice at low and high doses, and successfully incorporated in a physiologically-based pharmacokinetic model, together with transcapillary permeability and static receptor binding in extrahepatic tissues. For cyclopeptides, moreover, their brain penetration was shown to be modulated by P-glycoprotein-mediated efflux functioning at the blood-brain barrier. The kinetic RME analysis enables the prediction of not only the nonlinear target-mediated clearance and distribution of peptides, but also the down-regulation and subsequent recovery of surface receptors, which is useful for assessing the time-dependent changes of in vivo efficacy of peptide drugs. In conclusion, the therapeutic efficacy and protocols of peptide drugs should be assessed from its microscopic pharmacology based on the RME mechanisms, in conjunction with macroscopic pharmacokinetic modeling.
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